Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing Images
Semantic change detection (SCD) represents a challenging task in the interpretation of remote sensing images (RSIs), with the goal of identifying change regions and extracting semantic information from bitemporal RSIs simultaneously. The recent integration of deep neural networks leveraging multitas...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10737132/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850146528785596416 |
|---|---|
| author | Xibing Zuo Fei Jin Lei Ding Shuxiang Wang Yuzhun Lin Bing Liu Yao Ding |
| author_facet | Xibing Zuo Fei Jin Lei Ding Shuxiang Wang Yuzhun Lin Bing Liu Yao Ding |
| author_sort | Xibing Zuo |
| collection | DOAJ |
| description | Semantic change detection (SCD) represents a challenging task in the interpretation of remote sensing images (RSIs), with the goal of identifying change regions and extracting semantic information from bitemporal RSIs simultaneously. The recent integration of deep neural networks leveraging multitask learning has shown promise in enhancing SCD performance. However, there is still a challenge in improving SCD performance, specifically in designing a fine-grained network structure that can handle the two subtasks of change region localization and semantic information recognition in parallel. In this context, a novel multitask Siamese network, termed EGMS-Net, is proposed to boost the performance of SCD, which consists of three core components. First, a coarse-to-fine multitask Siamese network is constructed to obtain semantic information and change information at multiple levels. Second, an adaptive change information enhancement method based on spatial-spectral collaborative attention mechanism is proposed, which can assist the accurate localization of change regions without significantly increasing the model parameters. Third, a change information guidance module is developed to strengthen the interaction between multitask branches and reduce the difficulty of network training. Experiments on three benchmark datasets demonstrate that the proposed EGMS-Net outperforms existing state-of-the-art methods in the SCD community. |
| format | Article |
| id | doaj-art-236e9395ebf24a479407dd1c2dbefcb3 |
| institution | OA Journals |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-236e9395ebf24a479407dd1c2dbefcb32025-08-20T02:27:50ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352025-01-0118617710.1109/JSTARS.2024.348713710737132Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing ImagesXibing Zuo0https://orcid.org/0000-0002-8120-8692Fei Jin1https://orcid.org/0009-0000-4102-922XLei Ding2https://orcid.org/0000-0003-0653-8373Shuxiang Wang3https://orcid.org/0009-0004-1806-3402Yuzhun Lin4https://orcid.org/0000-0002-9560-1620Bing Liu5https://orcid.org/0000-0003-0848-8453Yao Ding6https://orcid.org/0000-0003-2040-2640Information Engineering University, Zhengzhou, ChinaInformation Engineering University, Zhengzhou, ChinaInformation Engineering University, Zhengzhou, ChinaInformation Engineering University, Zhengzhou, ChinaInformation Engineering University, Zhengzhou, ChinaInformation Engineering University, Zhengzhou, ChinaSchool of Optical Engineering, Xi'an Research Institute of High Technology, Xi'an, ChinaSemantic change detection (SCD) represents a challenging task in the interpretation of remote sensing images (RSIs), with the goal of identifying change regions and extracting semantic information from bitemporal RSIs simultaneously. The recent integration of deep neural networks leveraging multitask learning has shown promise in enhancing SCD performance. However, there is still a challenge in improving SCD performance, specifically in designing a fine-grained network structure that can handle the two subtasks of change region localization and semantic information recognition in parallel. In this context, a novel multitask Siamese network, termed EGMS-Net, is proposed to boost the performance of SCD, which consists of three core components. First, a coarse-to-fine multitask Siamese network is constructed to obtain semantic information and change information at multiple levels. Second, an adaptive change information enhancement method based on spatial-spectral collaborative attention mechanism is proposed, which can assist the accurate localization of change regions without significantly increasing the model parameters. Third, a change information guidance module is developed to strengthen the interaction between multitask branches and reduce the difficulty of network training. Experiments on three benchmark datasets demonstrate that the proposed EGMS-Net outperforms existing state-of-the-art methods in the SCD community.https://ieeexplore.ieee.org/document/10737132/Change information enhancementchange information guidancemultitask learningremote sensingsemantic change detection (SCD) |
| spellingShingle | Xibing Zuo Fei Jin Lei Ding Shuxiang Wang Yuzhun Lin Bing Liu Yao Ding Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing Images IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Change information enhancement change information guidance multitask learning remote sensing semantic change detection (SCD) |
| title | Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing Images |
| title_full | Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing Images |
| title_fullStr | Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing Images |
| title_full_unstemmed | Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing Images |
| title_short | Multitask Siamese Network Guided by Enhanced Change Information for Semantic Change Detection in Bitemporal Remote Sensing Images |
| title_sort | multitask siamese network guided by enhanced change information for semantic change detection in bitemporal remote sensing images |
| topic | Change information enhancement change information guidance multitask learning remote sensing semantic change detection (SCD) |
| url | https://ieeexplore.ieee.org/document/10737132/ |
| work_keys_str_mv | AT xibingzuo multitasksiamesenetworkguidedbyenhancedchangeinformationforsemanticchangedetectioninbitemporalremotesensingimages AT feijin multitasksiamesenetworkguidedbyenhancedchangeinformationforsemanticchangedetectioninbitemporalremotesensingimages AT leiding multitasksiamesenetworkguidedbyenhancedchangeinformationforsemanticchangedetectioninbitemporalremotesensingimages AT shuxiangwang multitasksiamesenetworkguidedbyenhancedchangeinformationforsemanticchangedetectioninbitemporalremotesensingimages AT yuzhunlin multitasksiamesenetworkguidedbyenhancedchangeinformationforsemanticchangedetectioninbitemporalremotesensingimages AT bingliu multitasksiamesenetworkguidedbyenhancedchangeinformationforsemanticchangedetectioninbitemporalremotesensingimages AT yaoding multitasksiamesenetworkguidedbyenhancedchangeinformationforsemanticchangedetectioninbitemporalremotesensingimages |